Privacy-Preserving Algorithms and Tools for Data Security
-
- Englisch ausgewählt
48,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
31.01.2027
Abbildungen
Approx. 70 p. 60 illus., 30 illus. in color.
Verlag
SpringerSeitenzahl
619
Maße (B/H)
15,5/23,5 cm
Sprache
Englisch
ISBN
978-3-032-44880-4
This book provides an authoritative exploration of privacy-preserving computation technologies and the modern security mechanisms that underpin them. In today’s data-driven era, data has become a core strategic resource across industries, yet the tension between data value extraction and privacy protection is increasingly evident. At the same time, frequent data breaches and growing public concern over personal information leakage have made privacy-preserving computation an indispensable foundation. By enabling data to be processed, analyzed, and utilized without exposing raw sensitive information, privacy-preserving computation strikes a balance between data-driven innovation and regulatory compliance, emerging as a key technology for unlocking data potential while reducing privacy risks.
To address the complex challenges in this field, the book systematically introduces a comprehensive set of theories, methods, and protocols. It begins with foundational topics, including the basic principles of privacy-preserving computation, threat models, and essential cryptographic primitives. It then progresses to modern cryptographic tools and frameworks, such as access control schemes, secure aggregation protocols, federated learning frameworks, and quantum-safe multi-party computation protocols.
The book also examines emerging trends and frontier applications of privacy-preserving computation across multiple domains, including finance, healthcare, industry, transportation, and smart homes, demonstrating its versatility. In finance, it enables secure data collaboration for risk assessment and fraud detection without exposing customer information. In healthcare, it supports cross-institutional medical data analysis while preserving patient privacy. In industrial data sharing, it facilitates secure inter-organizational exchange. These practical applications highlight the adaptability of privacy-preserving computation across sectors and validate its ability to balance efficiency, security, and compliance.
Comprehensive and practice-oriented, this volume is designed for a wide audience, including researchers in privacy-preserving computation, cryptographers, and data security professionals seeking technical insights. It is also a valuable resource for practitioners, scholars, and graduate students in related fields. Furthermore, it provides actionable guidance for professionals in finance, healthcare, and government sectors on privacy-preserving data utilization. By combining theoretical foundations with real-world applications, the book not only addresses current industry needs but also inspires future research by identifying emerging directions in the field of privacy-preserving computation.
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